1. These slides are additional material for TIES4451 Data Mining: Data Lecture 3 TIES445 Data mining Nov-Dec 2007 Sami Äyrämö

2. These slides are additional material for TIES4452 Data quality l GIGO – Garbage In, Garbage Out –Effectiveness of DM exercise depends on the quality of data l Data quality concerns –individual measurements (records and fields) –collections of observations l Sources of error are infinite –Human error (e.g., keyboard error) –Instrumentation failure  Inaccuare or imprecise –Inadequate specification of measurement or data collection process

3. These slides are additional material for TIES4453 Quality of individual measurements l Bias –the difference between the mean of the repeated measurements and the true value l Precision –variability of the repeated measurements (NOTE: precision is not the number of digits in record) l Accuracy –small bias and high precision (e.g., small variance) –e.g, repeated measurement of someone’s height may be precise (reliable), but inaccurate (validity), if (s)he is wearing shoes (we are not measuring the right thing) l True value (does it even exist?)

4. These slides are additional material for TIES4454 Quality of collections of data : bias l Distorted (biased) samples –mismatch between the sample population and and the population of interest (selection bias)  e.g., calculating an average age of students in Jyväskylä when the sample is restricted to female students –a sample may be selected through a chain of selection steps  e.g., candidates for bank loans: 1) potential customers are contacted, 2) some reply, some do not, 3) of those who replied some are creditworthy, some are not, 4) those who take out a loan are followed, 5) some are good customers, some are not,… –populations are not static (population drift)  e.g., customers shopping behaviour may change over time l A biased sample leads to inconsistent estimates of population parameters

5. These slides are additional material for TIES4455 Quality of collections of data: Incomplete data l Incomplete data: missing or empty values –Missing value: Information is not collected  e.g., People decline to answer a question (age, weight, position,…) – Empty value: Information does not exist  A form may have conditional parts: e.g., expiry date of an driver’s license can not be filled out by children –Determining whether any value is ”empty” or ”missing” requires domain knowledge  If the discriminating information is not provided both empty and missing values are treated as ”and called” missing –Fundamental question for data mining task: ”Why are the data incomplete?” –Note: A distorted (biased) sample is actually a special case of incomplete data